





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     List[str] 						=      cva.getAffineTransform(_lowerCamelCase							,_lowerCamelCase						)
		return cva.warpAffine(_lowerCamelCase							,_lowerCamelCase							,(rows, cols)						)


if __name__ == "__main__":
		# read original image
		_lowercase		:  Tuple			       =cva.imread(
		    str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
		)
		# turn image in gray scale value
		_lowercase		:  Tuple			       =cva.cvtColor(image, cva.COLOR_BGR2GRAY)
		# get image shape
		_lowercase		:  Optional[int]			       =gray_img.shape

		# set different points to rotate image
		_lowercase		:  Tuple			       =np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
		_lowercase		:  Union[str, Any]			       =np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
		_lowercase		:  int			       =np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
		_lowercase		:  str			       =np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

		# add all rotated images in a list
		_lowercase		:  Optional[Any]			       =[
		    gray_img,
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		]

		# plot different image rotations
		_lowercase		:  Optional[int]			       =plt.figure(1)
		_lowercase		:  Dict			       =['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
		for i, image in enumerate(images):
				plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
				plt.title(titles[i])
				plt.axis("""off""")
				plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
		plt.show()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)